2890. Reshape Data: Melt

DataFrame report

Column Name	Type
product	object
quarter_1	int
quarter_2	int
quarter_3	int
quarter_4	int

Instructions

• Write a solution to reshape the data so that each row represents sales data for a product in a specific quarter.
• The result format is in the following example.

Example

Input:

product	quarter_1	quarter_2	quarter_3	quarter_4
Umbrella	417	224	379	611
SleepingBag	800	936	93	875

Output:

product	quarter	sales
Umbrella	quarter_1	417
SleepingBag	quarter_1	800
Umbrella	quarter_2	224
SleepingBag	quarter_2	936
Umbrella	quarter_3	379
SleepingBag	quarter_3	93
Umbrella	quarter_4	611
SleepingBag	quarter_4	875

Explanation:

The DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.

Submissions

import pandas as pd

def meltTable(report: pd.DataFrame) -> pd.DataFrame:
    return report.melt(id_vars='product', var_name='quarter', value_name='sales')

Explanation

Python (Pandas)

Submitted by @noeyislearning

• import pandas as pd: This line imports the pandas library, which is a powerful tool for data manipulation and analysis in Python, and aliases it as pd.
• def meltTable(report: pd.DataFrame) -> pd.DataFrame:
  • meltTable is the name of the function.
  • It accepts one parameter, report, which is expected to be a pandas DataFrame. The type hint pd.DataFrame indicates the expected type of the parameter and the return type of the function.
  • The function is annotated to return a DataFrame.
• return report.melt(id_vars='product', var_name='quarter', value_name='sales'):
  • The melt method is called on the report DataFrame.
  • id_vars='product': This specifies the column(s) to use as identifier variables. In this case, the product column will remain as is, and the other columns will be "melted".
  • var_name='quarter': This sets the name of the new column that will contain the column headers from the original DataFrame, which are not specified in id_vars. Essentially, it's the name for the "variable" column in the melted DataFrame.
  • value_name='sales': This sets the name of the new column that will contain the values from the original DataFrame's columns that were melted. It's the name for the "value" column in the melted DataFrame.